Concepts of mental life across cultures: Supplemental analysis

In this notebook we conduct exploratory factor analyses (EFAs) on the datasets for our studies of concepts of mental life, in which each participants judged the various mental capacities of a particular target entity. We analyze datasets for adults and children from each of our five field sites: the US, Ghana, Thailand, China, and Vanuatu.

This notebook contains the results presented in the main text, in which we use Pearson correlations with our three-point response scale (no = 0, kinda = 0.5, yes = 1); see supplemental analyses for a version of these analyses treating kinda = yes = 1 and using tetrachoric correlations.

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     

Adults

Samples

  country   n
       US 127
    Ghana 150
 Thailand 150
    China 136
  Vanuatu 148
    Total 711

Scale use

Factor retention: parallel analysis

Exploratory factor analysis

Factor loadings

the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

Congruence

funs() is soft deprecated as of dplyr 0.8.0
Please use a list of either functions or lambdas: 

  # Simple named list: 
  list(mean = mean, median = median)

  # Auto named with `tibble::lst()`: 
  tibble::lst(mean, median)

  # Using lambdas
  list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once per session.

Bootstrapped congruence

Children

Samples

  country   n
       US 117
    Ghana 150
 Thailand 152
    China 131
  Vanuatu 143
    Total 693

Scale use

Factor retention: parallel analysis

Exploratory factor analysis

Factor loadings

Joining, by = c("factor", "country")

the condition has length > 1 and only the first element will be usedJoining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"
the condition has length > 1 and only the first element will be usedJoining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"
the condition has length > 1 and only the first element will be usedJoining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"
the condition has length > 1 and only the first element will be usedJoining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"
the condition has length > 1 and only the first element will be usedJoining, by = "capacity"
Joining, by = "factor"
Joining, by = "factor"

Joining, by = c("factor", "country", "age_group", "factor_name", "factor_descript", "factor_labdescript")

Congruence

See All samples, below.

Bootstrapped congruence

Joining, by = "capacity"
Joining, by = "factor_A"
Joining, by = "factor_B"

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All samples

Congruence

Joining, by = "factor_A"
Joining, by = "factor_B"

Joining, by = "factor_A"
Joining, by = "factor_B"

the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedVectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.the condition has length > 1 and only the first element will be usedthe condition has length > 1 and only the first element will be used

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Developmental comparisons

Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

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Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

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Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

Saving 8 x 9.6 in image
Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

Saving 8 x 9.6 in image

Joining, by = c("factor", "age_group", "country", "factor_name", "factor_descript", "factor_labdescript")
Joining, by = c("factor", "country", "age_group")
Column `country` joining character vector and factor, coercing into character vector

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